A LARGE LANGUAGE MODEL-BASED MULTI-AGENT SYSTEM FOR ENHANCED INFORMATION RETRIEVAL IN CIVIL ENGINEERING

Abstract: This paper presents a framework for information retrieval from the heterogeneous data sources in the construction sector. The core of the approach is a Large Language Model (LLM)-driven multi-agent system (MAS) consisting of specialized sub-agents and tools, each assigned to a specific data type. A host agent orchestrates the overall process. As a proof of concept, an implementation for Building Information Modeling (BIM) models and technical standards is presented. Challenges arise at the interfaces between LLM agents and the retrieval mechanisms. These are addressed by employing LangChain as an agent framework and the usage of the Model Context Protocol (MCP) as a novel standardized interface. The results demonstrate that such a MAS can answer complex queries that require information from multiple heterogeneous sources by combining several retrieval methods. The study provides a foundation for developing increasingly comprehensive systems through the application of various adaptation and optimization mechanisms.